minicpm5-finetune-xtuner

Fine-tune MiniCPM5-1B with xtuner (mmengine config-driven SFT). Use when the user mentions "xtuner", "mmengine", InternLM's training framework, or wants config-file-driven training.

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Fine-tune MiniCPM5-1B with xtuner

mmengine config-driven SFT. Uses Python config files (not YAML) and integrates with mmengine's runner / hook system.

Required input

Var	Example	Default
`BASE_MODEL`	`openbmb/MiniCPM5-1B`	required
`DATA`	path to messages-format jsonl	required
`WORK_DIR`	`./runs/minicpm5_xtuner`	required

Steps

1. Install (once)

pip install "xtuner==0.2.0"
# Replace opencv-python with the headless variant (avoids libGL linkage)
pip install --force-reinstall opencv-python-headless
pip uninstall -y opencv-python

2. Save the config — `${WORK_DIR}/minicpm5_lora.py`

import torch
from datasets import load_dataset
from mmengine.dataset import DefaultSampler
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
                            LoggerHook, ParamSchedulerHook)
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
from peft import LoraConfig
from torch.optim import AdamW
from transformers import AutoModelForCausalLM, AutoTokenizer

from xtuner.dataset import process_hf_dataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.dataset.map_fns import openai_map_fn, template_map_fn_factory
from xtuner.engine.hooks import DatasetInfoHook
from xtuner.engine.runner import TrainLoop
from xtuner.model import SupervisedFinetune
from xtuner.utils import PROMPT_TEMPLATE

pretrained_model_name_or_path = "${BASE_MODEL}"      # ← replace
data_path = "${DATA}"                                # ← replace
prompt_template = PROMPT_TEMPLATE.qwen_chat          # 🔑 ChatML — DO NOT use llama3_chat
max_length = 2048

batch_size = 4
accumulative_counts = 4
max_epochs = 2
lr = 2e-4
warmup_ratio = 0.03

tokenizer = dict(type=AutoTokenizer.from_pretrained,
                 pretrained_model_name_or_path=pretrained_model_name_or_path,
                 trust_remote_code=False, padding_side="right")

model = dict(
    type=SupervisedFinetune, use_varlen_attn=False,
    llm=dict(type=AutoModelForCausalLM.from_pretrained,
             pretrained_model_name_or_path=pretrained_model_name_or_path,
             trust_remote_code=False, torch_dtype=torch.bfloat16),
    lora=dict(type=LoraConfig, r=16, lora_alpha=32, lora_dropout=0.05,
              bias="none", task_type="CAUSAL_LM",
              target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]),
)

train_dataset = dict(
    type=process_hf_dataset,
    dataset=dict(type=load_dataset, path="json", data_files=dict(train=data_path)),
    tokenizer=tokenizer, max_length=max_length,
    dataset_map_fn=openai_map_fn,                                # 🔑 messages format
    template_map_fn=dict(type=template_map_fn_factory, template=prompt_template),
    remove_unused_columns=True, shuffle_before_pack=True,
    pack_to_max_length=False, use_varlen_attn=False,
)
train_dataloader = dict(
    batch_size=batch_size, num_workers=2,
    dataset=train_dataset,
    sampler=dict(type=DefaultSampler, shuffle=True),
    collate_fn=dict(type=default_collate_fn, use_varlen_attn=False),
)

optim_wrapper = dict(
    type=AmpOptimWrapper,
    optimizer=dict(type=AdamW, lr=lr, betas=(0.9, 0.999), weight_decay=0),
    clip_grad=dict(max_norm=1, error_if_nonfinite=False),
    accumulative_counts=accumulative_counts, loss_scale="dynamic", dtype="bfloat16",
)
param_scheduler = [
    dict(type=LinearLR, start_factor=1e-2,                       # 🔑 use 1e-2 not 1e-5 (default is too small)
         by_epoch=True, begin=0, end=warmup_ratio * max_epochs, convert_to_iter_based=True),
    dict(type=CosineAnnealingLR, eta_min=0.0, by_epoch=True,
         begin=warmup_ratio * max_epochs, end=max_epochs, convert_to_iter_based=True),
]
train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)

default_hooks = dict(
    timer=dict(type=IterTimerHook),
    logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
    param_scheduler=dict(type=ParamSchedulerHook),
    checkpoint=dict(type=CheckpointHook, by_epoch=False, interval=200, max_keep_ckpts=2),
    sampler_seed=dict(type=DistSamplerSeedHook),
)
custom_hooks = [dict(type=DatasetInfoHook, tokenizer=tokenizer)]
env_cfg = dict(cudnn_benchmark=False, mp_cfg=dict(mp_start_method="fork"), dist_cfg=dict(backend="nccl"))
log_level = "INFO"
load_from = None
resume = False
randomness = dict(seed=42, deterministic=False)
log_processor = dict(by_epoch=False)

🔑 PROMPT_TEMPLATE.qwen_chat is correct — that's the ChatML template. Do NOT use llama3_chat (which is <|start_header_id|>...<|eot_id|> and would corrupt every example). 🔑 start_factor=1e-2 for LinearLR — xtuner's default start_factor=1e-5 combined with convert_to_iter_based=True produces an effective LR of ~1e-9 (way too small).

3. Train — invoke `train.py` directly (not `xtuner train`)

CUDA_VISIBLE_DEVICES=0 python -m xtuner.tools.train ${WORK_DIR}/minicpm5_lora.py --work-dir ${WORK_DIR}

🔑 Use python -m xtuner.tools.train directly — this keeps the command inside the active venv / conda env and avoids the CLI wrapper spawning a different python.

For multi-GPU, use the standard wrapper: NPROC_PER_NODE=8 xtuner train CONFIG --work-dir ....

4. Validate

05/17 09:33:59 - mmengine - INFO - Num train samples 200
05/17 09:34:00 - mmengine - INFO - train example:
<s><|im_start|>system
你是 ...<|im_end|>
<|im_start|>user
...<|im_end|>
<|im_start|>assistant
...<|im_end|>

Iter(train) [10/100]  loss: 4.10
Iter(train) [50/100]  loss: 3.50
Saving checkpoint at 200 iterations

The chat template should resolve into proper <|im_start|>...<|im_end|> markers (visible from DatasetInfoHook output). Loss should decrease.

Convert pth → HF adapter

xtuner saves epoch_X.pth (mmengine format). Convert to PEFT adapter:

xtuner convert pth_to_hf ${WORK_DIR}/minicpm5_lora.py ${WORK_DIR}/iter_XXXX.pth ./adapter_hf

Then load with PEFT:

from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B", torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base, "./adapter_hf").eval()

Common pitfalls

libGL.so.1: cannot open shared object file: replace opencv-python → opencv-python-headless (see install step).
xtuner train hangs without logs: invoke train.py directly (see step 3).
Failed to import mmengine.runner: ALLOWED_LAYER_TYPES: transformers too new. Pin transformers==4.57.x.
Loss flat: scheduler LR underestimated. Use start_factor=1e-2 (see config above).

Reference

docs/finetune/xtuner.md

Repository: OpenBMB/MiniCPM
Path: skills/minicpm5-finetune-xtuner/SKILL.md
Commit: 719e4fc

Last updated: 17 days ago
First committed: 3 months ago

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